Procrustes-conditioned joint End-to-end Top-K SAEs recover more cross-seed universal features (r≥0.70) from independent BERT seeds than post-hoc alignment baselines on three NLP datasets.
Advances in Neural Information Processing Systems34, 5656–5668 (2021)
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Cross-seed explainability using Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencoders
Procrustes-conditioned joint End-to-end Top-K SAEs recover more cross-seed universal features (r≥0.70) from independent BERT seeds than post-hoc alignment baselines on three NLP datasets.